Update README.md
Browse files
README.md
CHANGED
@@ -4,7 +4,7 @@ datasets:
|
|
4 |
- deepghs/character_similarity
|
5 |
metrics:
|
6 |
- f1
|
7 |
-
pipeline_tag:
|
8 |
tags:
|
9 |
- art
|
10 |
---
|
@@ -24,4 +24,4 @@ tags:
|
|
24 |
|
25 |
* The calculation of `F1 Score`, `Precision`, and `Recall` considers "the characters in both images are the same" as a positive case. `Threshold` is determined by finding the maximum value on the F1 Score curve.
|
26 |
* `Cluster_2` represents the approximate optimal clustering solution obtained by tuning the eps value in DBSCAN clustering algorithm with min_samples set to `2`, and evaluating the similarity between the obtained clusters and the true distribution using the `random_adjust_score`.
|
27 |
-
* `Cluster_Free` represents the approximate optimal solution obtained by tuning the `max_eps` and `min_samples` values in the OPTICS clustering algorithm, and evaluating the similarity between the obtained clusters and the true distribution using the `random_adjust_score`.
|
|
|
4 |
- deepghs/character_similarity
|
5 |
metrics:
|
6 |
- f1
|
7 |
+
pipeline_tag: zero-shot-image-classification
|
8 |
tags:
|
9 |
- art
|
10 |
---
|
|
|
24 |
|
25 |
* The calculation of `F1 Score`, `Precision`, and `Recall` considers "the characters in both images are the same" as a positive case. `Threshold` is determined by finding the maximum value on the F1 Score curve.
|
26 |
* `Cluster_2` represents the approximate optimal clustering solution obtained by tuning the eps value in DBSCAN clustering algorithm with min_samples set to `2`, and evaluating the similarity between the obtained clusters and the true distribution using the `random_adjust_score`.
|
27 |
+
* `Cluster_Free` represents the approximate optimal solution obtained by tuning the `max_eps` and `min_samples` values in the OPTICS clustering algorithm, and evaluating the similarity between the obtained clusters and the true distribution using the `random_adjust_score`.
|